As medical image acquisition systems become more prevalent, many healthcare professionals, such as radiologists and physicians, face the problem that the time available for the examination of the images decreases. Consequently, there is a growing need for diagnosis support systems to assist in the examination.
One of the tools commonly used is to access records related to previous cases. These records may comprise medical images and textual medical reports. For ease of storage and retrieval, these records are collected in databases which may take many forms, such as local folders on a computer system, accessible to individual users or to multiple users, PACS systems, a reference case manager like mypacs.net (www.mypacs.net).
These databases comprise many reports, far exceeding the number of cases that any single person can recall. Even a personal folder, populated by one healthcare professional such as a radiologist, will typically grow beyond the point where its user can recall its full content. The problem is compounded by the access to databases of multiple users and multiple healthcare disciplines. The ability to search the databases and retrieve relevant medical images therefore becomes increasingly important. As medical imaging becomes more affordable, and the diversity of diagnostic modalities and therapeutic treatments increase, the amount of data being stored increases, and the problem becomes even more critical.
One approach to improve retrieval efficiency of images is to employ semantics to establish a defined set of search and classification terms. Such semantics systems include UMLS (Unified Medical Language System) and Radlex (a lexicon for retrieval of radiology information resources). However, such semantics systems still require the user to make a selection of the most appropriate term or terms to classify a report or image, and the accuracy of the results are thus dependent on the skill and knowledge of the classifier.
An attempt at a semantic indexing system for medical reports and medical images is disclosed in “A Semantic Fusion Approach Between Medical Images and Reports Using ULMS”, Racoceanu, Lacoste, Teodorescu, Vuillemenot, Third Asia Information Retrieval Symposium, AIRS 2006, Singapore, Oct. 16-18, 2006. This system indexes reports by determining the frequency of semantic concepts in the text and applying a weighting appropriately. Images are indexed by first defining a visual vocabulary and then optionally weighting the semantic concepts depending on where they are found in the image. For example, a semantic concept detected in the centre of an image is considered more important. The visual vocabulary is a visual representation in terms of color, texture and shape, which is to be searched for in the image records.
Unfortunately, creating the visual vocabulary requires a great deal of user guidance in selecting the images.